Redbird AI syncs dbt transformation artifacts, compiled models, and test results directly to Google Cloud Storage — automatically staging datasets, publishing documentation, and archiving model outputs. Stop manually uploading dbt artifacts or running custom scripts to move transformed data between your warehouse and GCS buckets.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Automatically upload dbt's compiled SQL, manifest.json, and run_results.json to designated GCS buckets whenever models run. Orchestration tools and downstream processes access the latest transformation logic without manual artifact management.
Export finalized dbt model outputs to Google Cloud Storage as partitioned Parquet files for ML pipelines or cross-platform sharing. Analytics engineers deliver transformation results to data science teams without warehouse-to-warehouse exports.
Automatically generate and upload dbt docs to a Cloud Storage bucket configured for static website hosting whenever documentation changes. Stakeholders access current data lineage and model definitions without running local dbt commands.
Store timestamped test failures, warnings, and data quality metrics in organized GCS folders for long-term auditing. Data teams maintain historical quality records without bloating the warehouse with test metadata.
Automatically kick off dbt model runs when upstream data files appear in specified Cloud Storage locations. Transformation pipelines stay synchronized with data arrival without polling or manual scheduling.
Monitor designated GCS folders for updated CSV reference files and automatically sync them into dbt seed directories. Analytics engineers maintain dimension tables and lookup data without manual file transfers.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize dbt and Google Cloud Storage with OAuth or API credentials. Redbird never stores your data — it just passes through.
Tell Redbird what to do in plain language — no SQL, no code, no configuration files required.
Redbird shows you exactly what it will do before running anything. Approve the workflow, set a schedule, and switch it on.
Workflows run on your schedule or on triggers. Every run is logged. Adjust with natural language at any time.
Redbird AI understands both dbt's transformation artifacts and Google Cloud Storage's bucket organization — intelligently mapping model outputs, test results, and documentation structures to the right GCS paths.
Redbird reads dbt's manifest.json, catalog.json, and run_results.json to understand model dependencies, test outcomes, and schema definitions. It automatically organizes these artifacts in GCS with sensible folder structures — timestamped run artifacts, versioned documentation, and partitioned dataset exports. The AI recognizes when dbt models target BigQuery tables and coordinates staging transformed data back to GCS for broader ecosystem access, maintaining proper naming conventions and lifecycle policies.
faster than building custom dbt hooks and GCS upload scripts
Redbird can pull from dbt and Google Cloud Storage simultaneously, merge the results, and format a polished report — sent on a schedule or on demand.
Set conditions in natural language. Get notified in Slack or email the moment a threshold is crossed in either dbt or Google Cloud Storage.
SOC 2 Type II certified. Data flows encrypted in transit and at rest. Fine-grained permission controls with full audit logs.
Push data from dbt into Google Cloud Storage, or from Google Cloud Storage back into dbt. Resolve conflicts with configurable merge rules.
Every workflow run is logged — what ran, what changed, and why. Replay or revert any individual step at any time.
Start workflows from any dbt run event or GCS bucket change — Redbird connects the entire transformation-to-storage lifecycle.
Fires when a dbt model or group of models finishes running successfully.
Triggers when data quality tests produce failures or warnings during execution.
Activates when dbt docs generate command creates updated lineage documentation.
Execute a targeted dbt model or tag-based model selection.
Update dbt source freshness checks and validate upstream data availability.
Compile dbt project and produce manifest.json with current model graph.
Fires when new objects land in specified Cloud Storage buckets or prefixes.
Triggers when file metadata, labels, or custom attributes change in GCS.
Activates when GCS lifecycle policies archive, delete, or transition object storage classes.
Write files to specified Cloud Storage locations with custom metadata and permissions.
Generate time-limited access URLs for private GCS objects.
Move or duplicate files across GCS buckets with preserved metadata.
Sync dbt transformations with Google Cloud Storage automatically. Redbird AI handles artifact publishing, dataset staging, and documentation delivery so your analytics engineers focus on building models, not moving files.